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Quantitative modeling of chronic myeloid leukemia: insights from radiobiology by Tomas Radivoyevitch, Lynn Hlatky, Julian Landaw, and Rainer K. Sachs Blood.

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Presentation on theme: "Quantitative modeling of chronic myeloid leukemia: insights from radiobiology by Tomas Radivoyevitch, Lynn Hlatky, Julian Landaw, and Rainer K. Sachs Blood."— Presentation transcript:

1 Quantitative modeling of chronic myeloid leukemia: insights from radiobiology by Tomas Radivoyevitch, Lynn Hlatky, Julian Landaw, and Rainer K. Sachs Blood Volume 119(19):4363-4371 May 10, 2012 ©2012 by American Society of Hematology

2 Cell population dynamic CML models. Tomas Radivoyevitch et al. Blood 2012;119:4363-4371 ©2012 by American Society of Hematology

3 TKI discontinuation possibilities illustrate deterministic versus stochastic modeling differences. Tomas Radivoyevitch et al. Blood 2012;119:4363-4371 ©2012 by American Society of Hematology

4 CML latency time T. T is here considered as a random variable, that is, a quantity described by a probability density because it may fluctuate from case to case in a way not (yet) fully predictable. Tomas Radivoyevitch et al. Blood 2012;119:4363-4371 ©2012 by American Society of Hematology

5 Atomic bomb survivor waiting times. Tomas Radivoyevitch et al. Blood 2012;119:4363-4371 ©2012 by American Society of Hematology

6 Age dependence of background CML incidence and translocation prevalence. Tomas Radivoyevitch et al. Blood 2012;119:4363-4371 ©2012 by American Society of Hematology

7 CML incidence versus age at radiation exposure for different dose groups. Tomas Radivoyevitch et al. Blood 2012;119:4363-4371 ©2012 by American Society of Hematology

8 Proximity effects. Tomas Radivoyevitch et al. Blood 2012;119:4363-4371 ©2012 by American Society of Hematology


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